Research On TXX UAV Automatic Tracking Algorithm

Compared with manned aircraft, UAV has many advantages, such as low cost, light weight, small volume, simplicity of operation, usableness, strong survivability, good stealth, low requirements for battle environment, and it can perform various military or civilian tasks accurately, efficiently and agilely. UAV is strongly favored by the countries all over the world. However, UAV must be range tested to ensure performance stable before actual application. Because of unmanned driving, UAV needs to be tracked accurately by researchers on the ground, which can help people to understand the state of the UAV.TXX UAV automatic tracking algorithm based on cooperative object is designed in this thesis. What can be realized by this algorithm are image preprocessing, target detection and recognition of image sequence, extraction of target useful information on the basis of the image threshold segmented, prediction of target track by using moving model of the target, and target searching near the prediction location.The tracking schemes of UAV are analyzed and compared, with the cooperative object being used to cooperate to track UAV. By the contrast and analysis of two classical de-noising methods, the improved median filter method is used to remove the noise off the image. Mathematical morphology processing is applied to adjust the brightness details to make de-noising image smoother. Target detection methods are introduced, and layering voting is used to identify the verity of target. The image containing true target is threshold segmented through improved Otsu method based on iteration. The facula can be extracted from the image completely and then be located. Target track is predicted by Kalman filtering algorithm. Near the prediction position, whether the current search area matches with the target template can be judged by using the K-S test based on histogram information, and simulation results are given. Finally, coordinate system and their transformation relations in tracking process are presented, and the attitude adjustment principle of lasers and CCD camera is given.The experiment and simulation results indicated that the noise was removed off the image faster through median filter based on selection sort, and the problem that the target and background were segmented mistakenly was solved by improved Otsu method. After target recognition, the target track could be predicted effectively by using Kalman filtering algorithm. The optimal matched position was found near the prediction position through the histogram matching method based on K-S test. Finally, target tracking could be realized with this tracking algorithm. The requirement of target tracking was satisfied by this target tracking algorithm. The target could be recognized effectively, and also the efficiency of target tracking was improved.